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Balduíno Mateus
CISE—Electromechatronic Systems Research Centre, University of Beira Interior, Calçada Fonte do Lameiro, P–62001-001 Covilhã, Portugal

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Journal article
Published: 30 June 2021 in Applied Sciences
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Predictive maintenance is very important in industrial plants to support decisions aiming to maximize maintenance investments and equipment’s availability. This paper presents predictive models based on long short-term memory neural networks, applied to a dataset of sensor readings. The aim is to forecast future equipment statuses based on data from an industrial paper press. The datasets contain data from a three-year period. Data are pre-processed and the neural networks are optimized to minimize prediction errors. The results show that it is possible to predict future behavior up to one month in advance with reasonable confidence. Based on these results, it is possible to anticipate and optimize maintenance decisions, as well as continue research to improve the reliability of the model.

ACS Style

Balduíno Mateus; Mateus Mendes; José Farinha; António Cardoso. Anticipating Future Behavior of an Industrial Press Using LSTM Networks. Applied Sciences 2021, 11, 6101 .

AMA Style

Balduíno Mateus, Mateus Mendes, José Farinha, António Cardoso. Anticipating Future Behavior of an Industrial Press Using LSTM Networks. Applied Sciences. 2021; 11 (13):6101.

Chicago/Turabian Style

Balduíno Mateus; Mateus Mendes; José Farinha; António Cardoso. 2021. "Anticipating Future Behavior of an Industrial Press Using LSTM Networks." Applied Sciences 11, no. 13: 6101.